A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

• set -e, -u, -x, -o pipefail
• set -e
• set -x
• set -u
• set -o pipefail
• Setting IFS
• Original Reference

• Best 'always' free tier cloud platforms
  • A word of caution
  • What are my (maybe yours too) needs and wants?
  • Eliminating useless choices
    • Heroku
    • AWS
    • Azure
• Google Cloud

All the boxes are single/two cell tables or two row tables.

This is a writedown of how I installed my raspberry pi(s).

The base is a Proxmox, so I don't have to worry about reformat the SD card every time I wanna try out something new, as I can start VMs and LXC containers, as well as Docker containers once it's configured properly.

Note: Those files are versioned, so you can always look what changed from time to time.

• Pimox

Remove this condition "when": "workspacePlatform == windows"

%USERPROFILE%\.cursor\extensions\ms-vscode.cpptools-1.23.5-win32-x64\package.json

    "type": "cppvsdbg",
    "label": "C++ (Windows)",
    "when1": "workspacePlatform == windows",